Caching generally refers to the local storage of data obtained from a remote source to streamline access to this data. FIG. 1 is a network diagram that shows a typical architecture for caching data. It shows a number of data provider nodes such as data providers 110, 120, and 130 that each provide data to a number of data consumer nodes. For example, data provider 110 provides data to consumers including consumers 112 and 113. Each data provider further has a local cache. For example, provider 110 has local cache 111. When a data provider receives a primary data request from one of its consumers, it first checks its local cache to see whether the primary data request can be satisfied from the cache. If so, the provider responds to the primary data request with a response that is based upon cache contents; otherwise, the provider (1) issues a secondary data request to an authoritative data source, (2) responds to the primary data request with a response that is based upon data provided by the data source in response to the secondary data request, and (3) stores the data provided by the data source in response to the secondary data request as an entry in its local cache for use to satisfy future primary data requests. In some cases (not shown), multiple providers may share the same cache. In some cases (not shown), a cache may manage itself, or may be managed by an entity other than the corresponding data provider.
As one example, each data consumer may be a web client requesting web pages or other resources from data providers that are web servers. Data sources may be central data stores or services that provide data that enables the web servers to construct web pages or other resources requested by web clients. Such an architecture employing local caching is described in U.S. patent application Ser. No. 11/177,206, filed on Jul. 8, 2005, which is hereby incorporated by reference in its entirety. Alternatively, data providers may be proxy servers or other intermediate caches—such as caches stored on the same computer system that the web client executes on—that are for one or more web clients and web servers.
The level of efficiency provided by caching data in an environment such as is described above is heavily dependent on how each cache is managed—in particular, the basis used by the cache and/or its data provider to determine for how long a version of a data object stored in a cache entry should be used to satisfy primary requests.
Conventional schemes for maintaining the freshness of cache entries have significant disadvantages. A first conventional scheme involves the use of an object lifetime for each cached data object—a maximum amount of time that the data object should be kept in a cache before expiring the entry containing the object (sometimes referred to as the object's “time to live”). In this scheme, either a data source supplying the data object assigns such an amount of time to the object, or the data provider assigns an amount of time. When a cache entry containing the data object is added to the cache, the assigned amount of time is added to the current time to establish an expiration time for that entry. After the entry's expiration time passes, the entry is expired, and can no longer be retrieved from the cache, and the space it occupies in the cache can be reclaimed for new cache entries.
It is often difficult to select object lifetimes that produce good results. On one hand, if an object lifetime is selected that is too long, the cached object could become “stale”—i.e., it no longer matches the object that the data source that provided it would provide in response to a new secondary data request for the object—and it is of less value to requesting data consumers. On the other hand, if an object lifetime is selected that is too short, the likelihood that the cached object will ever be used to satisfy a primary data request is low, and little benefit is derived from caching the object.
A second conventional scheme for maintaining the freshness of cache entries involves the use of explicit invalidations. Explicit invalidations are notifications sent by a data source to data providers when the data source determines that the version of an object that it would provide in response to a secondary data request has changed, and any cached earlier-provided versions of that object provided by the data source are now stale. When a data provider receives an explicit invalidation notification from a data source, it immediately invalidates the referenced object from its cache, if it is contained there.
While the explicit invalidation scheme has the capacity to overcome the disadvantages associated with the object lifetime scheme, the explicit invalidation scheme has the disadvantage that it consumes substantial processing and/or communication resources. First, detecting a change in the object and sending explicit invalidations often consumes significant processing power on the part of the data source. Once the data source has detected this change, it can either (1) transmit an explicit invalidation to all data providers, incurring significant network bandwidth to convey the explicit invalidation and significant data provider processing costs to act on the explicit invalidation, or (2) itself spend additional processing and storage resource determining which data providers are most likely to have a stale version of the object cached, and send explicit invalidations to only those data providers.
An approach to maintaining the freshness of cache entries that overcame these disadvantages of conventional schemes for invalidating cache entries would have significant utility.